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	Use labels in tagger
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					@ -266,7 +266,7 @@ class Tagger(Pipe):
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            raise ValueError("nan value when computing loss")
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					            raise ValueError("nan value when computing loss")
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        return float(loss), d_scores
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					        return float(loss), d_scores
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    def initialize(self, get_examples, *, nlp=None):
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					    def initialize(self, get_examples, *, nlp=None, labels=None):
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        """Initialize the pipe for training, using a representative set
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					        """Initialize the pipe for training, using a representative set
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        of data examples.
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					        of data examples.
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					@ -277,15 +277,19 @@ class Tagger(Pipe):
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        DOCS: https://nightly.spacy.io/api/tagger#initialize
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					        DOCS: https://nightly.spacy.io/api/tagger#initialize
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        """
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					        """
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        self._ensure_examples(get_examples)
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					        self._ensure_examples(get_examples)
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					        if labels is not None:
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					            for tag in labels:
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					                self.add_label(tag)
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					        else:
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					            tags = set()
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					            for example in get_examples():
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					                for token in example.y:
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					                    if token.tag_:
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					                        tags.add(token.tag_)
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					            for tag in sorted(tags):
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					                self.add_label(tag)
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        doc_sample = []
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					        doc_sample = []
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        label_sample = []
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					        label_sample = []
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        tags = set()
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        for example in get_examples():
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            for token in example.y:
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                if token.tag_:
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                    tags.add(token.tag_)
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        for tag in sorted(tags):
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            self.add_label(tag)
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        for example in islice(get_examples(), 10):
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					        for example in islice(get_examples(), 10):
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            doc_sample.append(example.x)
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					            doc_sample.append(example.x)
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            gold_tags = example.get_aligned("TAG", as_string=True)
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					            gold_tags = example.get_aligned("TAG", as_string=True)
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